Identifying Failure Root Causes for Cloud-Native Microservice Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud-native microservice applications depend on reliable platforms to ensure stable performance, even under resource overload faults. However, understanding the root causes of system failures holistically remains a significant challenge. This paper proposes a novel, root cause-oriented framework that supports autonomic, self-managing systems with humans in the loop. Our approach leverages a three-fold modality of observability data—logs, metrics, and traces—to build a multi-perspective view of system behavior. We enhance preprocessing to extract metric anomaly scores and log semantics (e.g., Template ID counts and Golden Signal counts), which are then fused to train a GNN-GRU model. This model captures spatial and temporal patterns across services to classify failure types and identify the root causes behind them. The resulting root cause predictions—including correlated anomalies and their associated source and target services—are analyzed to provide context-rich insights, aiding human operators (e.g., SREs) in debugging and diagnosis. Our framework fits naturally into the Monitor-Analyze-Plan-Execute (MAPE) loop, enabling proactive fault management and feedback-driven improvement. Evaluations using the public MicroSS dataset—comprising faults like resource saturation and configuration errors—demonstrate the effectiveness of our method in accurately identifying failure origins and supporting operational resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it